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Bjak

Applied AI Engineer (US)

United States (Remote)fulltimemidAdded today

About this role

BJAK is hiring an Applied AI Engineer to build production AI systems for their neobank platform, focusing on real-world automation of financial workflows like onboarding, KYC, and customer support. This is a hands-on engineering role requiring experience shipping AI agents and LLM applications, not research-focused work.

What you'll do

  • Design and build AI agents, workflows, and automation tools that solve customer and operational problems in financial services
  • Implement LLM-based features including retrieval systems, tool calling, evaluations, and guardrails for production use
  • Develop AI-assisted experiences for onboarding, KYC, risk review, support automation, and document handling
  • Partner with product and engineering teams to identify high-impact automation opportunities and ship solutions quickly
  • Engineer reliable, scalable systems that handle constraints around accuracy, latency, cost, security, and compliance
  • Test, evaluate, and continuously improve AI systems in production to ensure quality and user trust

What they're looking for

  • Python, TypeScript, or JavaScript development
  • Building LLM applications, agents, and RAG systems
  • AI workflow automation and copilot design
  • Prompt engineering and model evaluation techniques
  • Production system reliability and testing
  • Fintech, banking, or operations automation domain knowledge
  • Risk, KYC, fraud, or support automation experience
  • Cross-functional collaboration with product and operations teams
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Bjak

Bjak is a Southeast Asian fintech super app offering insurance, payments, savings, wallets, and investment products through a unified platform. The company is hiring full stack engineers, backend engineers, iOS developers, and Android engineers to build scalable features and reliable systems across mobile and web products.

Website
bjak.com
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Likely interview questions

  • Walk us through an AI system you shipped to production—what was the workflow, what guardrails did you implement, and how did you measure reliability?
  • Describe a time when you had to decide whether a process should be automated with AI or handled manually. What was your reasoning?
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